Reliable personal identification is critical in many applications in order to prevent identity fraud.
The losses due to identity fraud can be substantial in terms of money and confidentiality. Traditionally, Automatic Authentication Systems have been employed in applications such as criminal identification, access control systems, and large-scale social service or national identity registry applications. With the emergence of new applications in E-Commerce there is a renewed interest in fast and accurate personal identification. For example, in applications such as web-based retailing, autonomous vending, and automated banking, authentication of consumer identity is critical to prevent fraud. In many of the new applications, the amount of time taken for identification is an important issue along with accuracy since the applications are real-time. While many authentication schemes have been proposed and implemented, biometrics is emerging as the most foolproof method of automated personal identification. The relative permanence of physiological/behavioral characteristics, the fact that biometrics cannot be lost or misplaced, and uniqueness over a large population have been cited as the distinct advantages of biometrics over other authentication mechanisms. Traditionally, fingerprints have been the most widely used and trusted biometrics. The ease of acquisition of fingerprints, the availability of inexpensive fingerprint sensors and a long history of usage in personal identification make fingerprints the most acceptable form of authentication.
A fingerprint is characterized by smoothly flowing ridges and valleys. The patterns formed by the alternating ridges and valleys have been verified to be unique to each person over a large population and have been used for personal verification over the past few centuries, primarily by the forensic community. For automated fingerprint verification a compact representation of the rich topology of valleys and ridges is desirable. Most automated fingerprint verification systems extract features from the fingerprint images and use the feature sets for verification. The naturally occurring ridge anomalies called the minutiae capture the essential features needed for automated verification. There are two basic types of ridge anomalies—ridge termination and ridge bifurcation. The number of minutiae in a fingerprint varies from print to print and typically a feature extractor reports 30–60 minutiae per print.
A generic automatic fingerprint identification system (AFIS), shown in FIG. 1 consists of four modules (1) image acquisition 100, (2) preprocessing (enhancement) 110, (3) feature extraction 120, and (4) verification or identification via feature matching 140. Typically a fingerprint is captured either by scanning an inked impression of a finger or using a live-scan fingerprint scanner. Preprocessing is often necessary to improve the quality of the fingerprint so that fingerprint features are properly extracted from the image. Finally, authentication is performed by matching the acquired features with those of a database fingerprint.
An ideal sensed or scanned fingerprint image is characterized by smoothly flowing patterns of distinct ridges and valleys. There are two prevalent and well accepted classes of fingerprint features arising from local ridge discontinuities (also known as minutiae): (1) ridge endings and (2) ridge bifurcations. Feature matching is accomplished by comparing the global and/or local distributions of the minutiae properties. Often, the imaging limitations, acquisition condition, age, maintenance of the original impression, as well as skin characteristics cause the acquired image to be far from ideal fingerprints. It is, therefore, desirable to enhance the sensed image, and ensure proper performance of the successive feature extraction and matching modules.
Traditionally, the forensic applications have been the biggest end-users of fingerprint enhancement algorithms, since the important ridge details are frequently obliterated in the latent fingerprints lifted from scene of crime. Being able to enhance these details and/or being able to detect the minutiae can sometimes mean a difference between catching a criminal or not. Also, with the increasing emphasis on the identity fraud in our highly automated and Internet-dependent world, the civil applications (e.g., access control, transaction authorization, social security management, etc.) will need fingerprint enhancement algorithms. In order that positive personal identification is achieved using biometrics, it is imperative that every subject be reliably identified/authenticated using the given choice of biometrics. Some kind of exception processing based (e.g., possession or knowledge) authentication for poor fingerprints poses threat to the integrity of the system and defeats the purpose using biometrics based authentication. Consequently, an AFIS should be able to make the best out of the available data and be able to match every (e.g., you can not afford to annoy a bank customer by asking multiple presentations) biometrics measurement as it is being offered. It is widely known that at least 2–5% of target population have poor-quality (in oriented sinusoid sense) fingerprints: fingerprints that cannot be reliably processed using automatic image processing methods. We suspect this fraction is even higher in reality when the target population consists of (i) older people; (ii) people who suffer routine finger injuries in their occupation; (iii) people living in dry weather or having skin problems, and (iv) people who can have poor fingerprints due to their genetic and racial attributes. With the increasing demand for cheaper and compact fingerprint scanners, the fingerprint verification software cannot afford the luxury of assuming good quality fingerprints obtained from the optical scanner. The cheaper semiconductor scanners not only offer smaller scan area but also significantly poorer quality fingerprints.